Method

ABSTRACT

The present invention describes a new functional biomarker of vascular inflammation and its use in predicting all-cause or cardiac mortality. The invention also provides a method for stratifying patients according to their risk of all-cause or cardiac mortality using data gathered from a computer tomography scans of a blood vessel to determine a specific combination of structural and functional biomarkers of vascular inflammation and disease.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.17/454,540 filed on Nov. 11, 2021, which is a divisional of U.S. patentapplication Ser. No. 16/345,165 filed on Apr. 25, 2019, which is theU.S. National Stage under 35 U.S.C. § 371 of PCT App. No.PCT/GB2017/053262 filed on Oct. 31, 2017, which in turn claims thebenefit of GB Patent App. No. 1620494.3 filed on Dec. 2, 2016 and GRPatent App. No. 20160100555 filed on Oct. 31, 2016. Each of theforegoing applications is hereby incorporated by reference in theirentireties.

FIELD OF THE INVENTION

The present invention relates to novel biomarkers of cardiovascular riskand methods for predicting all-cause mortality and cardiac events,including cardiac mortality.

BACKGROUND OF THE INVENTION

Atherosclerosis is a progressive process in which an artery wallthickens as a result of invasion and accumulation of white blood cells.This inflammatory process results in plaques within the vessel wallcontaining living white blood cells, dead cell debris and fatty depositsincluding cholesterol and triglycerides.

Stable atherosclerotic plaques, which tend to be asymptomatic, aretypically rich in extracellular matrix and smooth muscle cells, whileunstable plaques are rich in macrophages and foam cells and theextracellular matrix separating the lesion from the arterial lumen (alsoknown as the fibrous cap) is usually weak and prone to rupture. Rupturesof the fibrous cap eventually induce clot formation in the lumen, andsuch clots can block arteries or detach, move into the circulation andeventually block smaller downstream vessels causing thromboembolism.Chronically expanding plaques are frequently asymptomatic until vesselocclusion (stenosis) is severe enough that blood supply to downstreamtissue is insufficient.

Atherosclerosis is asymptomatic for decades because the arteries enlargeat all plaque locations and blood flow is not immediately affected.Indeed, plaque ruptures are also asymptomatic unless they result insufficient narrowing or closure of an artery that impedes blood flow todifferent organs so as to induce symptoms. Typically, the disease isonly diagnosed when the patient experiences other cardiovasculardisorders such as stroke or heart attack. Symptomatic atherosclerosis istypically associated with men in their 40s and women in their 50s to60s. Sub-clinically, the disease begins to appear in childhood, andnoticeable signs can begin developing at puberty. While coronary arterydisease is more prevalent in men than women, atherosclerosis of thecerebral arteries and strokes equally affect both sexes.

Atherosclerosis may cause narrowing in the coronary arteries, which areresponsible for bringing oxygenated blood to the heart, and this canproduce symptoms such as the chest pain of angina, shortness of breath,sweating, nausea, dizziness or light-headedness, breathlessness orpalpitations. Cardiac arrhythmias may also result from cardiac ischemia.Atherosclerosis that causes narrowing in the carotid arteries, whichsupply blood to the brain and neck, can produce symptoms such as afeeling of weakness, not being able to think straight, difficultyspeaking, becoming dizzy and difficulty in walking or standing upstraight, blurred vision, numbness of the face, arms, and legs, severeheadache and losing consciousness. These symptoms may also be present instroke, which is caused by marked narrowing or closure of arteries goingto the brain leading to brain ischemia and death of cells in the brain.Peripheral arteries, which supply blood to the legs, arms, and pelvismay also be affected. Symptoms can include numbness within the affectedlimbs, as well as pain. Plaque formation may also occur in the renalarteries, which supply blood to the kidneys. Plaque occurrence andaccumulation leads to decreased kidney blood flow and chronic kidneydisease, which, like all other areas, are typically asymptomatic untillate stages.

Vascular inflammation is a key feature in atherogenesis and plays acritical role in atherosclerotic plaque stability by triggering plaquerupture leading to acute coronary syndromes (see Ross R. N Engl J Med1999; 340:115-26, and Major A S et al Circulation 2011; 124:2809-11).Importantly, more than 50% of acute coronary syndromes are caused byhighly inflamed but anatomically non-significant atherosclerotic plaques(Fishbein M C et al. Circulation 1996; 94:2662-6), which are notidentifiable by any of the existing clinical diagnostic tests.

Early, non-invasive diagnosis of vascular inflammation has been hailedas the “holy grail” of cardiovascular diagnostics and could help improverisk stratification in primary and secondary prevention. However, thecurrent state-of-the-art methods for the diagnosis of vascularinflammation and cardiovascular risk prediction are suboptimal and haveseveral limitations. Circulating inflammatory biomarkers (e.g. CRP,TNF-α) have a limited value in cardiovascular risk prediction since theyare not specific to the cardiovascular system and have poor correlationwith local vascular inflammation (see Weintraub et al. Eur Heart J 2000;21:958-60; Lee R et al. Current medicinal chemistry 2012; 19:2504-20;and Margaritis M et al. Circulation 2013; 127:2209-21).

In the field of cardiovascular imaging, the predictive value of Agatstoncoronary calcium score measured by CT has been long-established.However, coronary calcification represents a non-reversible process thatdoes not change in response to appropriate medical therapy (e.g.statins) (Alexopoulos N et al. Journal of the American College ofCardiology 2013; 61:1956-61). In fact, calcified plaques are consideredmore stable and less likely to rupture compared to plaques withhigh-risk features, such as a thin-cap fibroatheromas and a largenecrotic core (Huang H et al. Circulation 2001; 103:1051-6). Detectionof high-risk plaque features such as microcalcification, a largenecrotic core or positive remodelling on CT angiograms have all beenshown to predict future cardiac events (Hecht H S et al. JACC CardiovascImaging 2015; 8:1336-9; and Saremi F et al. AJR Am J Roentgenol 2015;204:W249-60), but the reliability of the method is affected by theobserver's expertise and CT settings and parameters, including spatialresolution (Maurovich-Horvat P et al. Nat Rev Cardiol 2014; 11:390-402;and Fleg J L et al. JACC Cardiovasc Imaging 2012; 5:941-55).

Newer invasive methods such as optical coherence tomography (OCT) andintravascular ultrasound (IVUS) have been more successful in detectinghigh-risk plaques but are invasive, expensive, carry a small yetsignificant risk of in-procedure complications (Bezerra H G et al. JACCCardiovasc Interv 2009; 2:1035-46; and McDaniel M C et al. JACCCardiovasc Interv 2011; 4:1155-67), and are therefore not eligible forprimary prevention and wide screening of low-risk individuals. Positronemission tomography (PET) with ¹⁸F-FDG is expensive, associated withsignificantly higher levels of radiation exposure compared to CT alone,not readily available and limited by myocardial uptake of theradiotracer that results in significant background noise (Joshi N V etal. Lancet 2014; 383:705-13; and Rogers I S et al. Curr Cardiol Rep2011; 13:138-44). Even the introduction of newer radiotracers (such as¹⁸F—NaF), although promising, carries many of the limitations oftraditional PET imaging, including but not limited to significantradiation exposure, limited availability and no demonstrated value inprimary or even secondary prevention (Joshi N V et al. Lancet 2014;383:705-13).

Perivascular adipose tissue (PVAT) surrounds (coronary) arteries and maybe involved in local stimulation of atherosclerotic plaque formation.PVAT can be quantified using a number of techniques, including forexample, echocardiography, computed tomography (CT) and magneticresonance imaging (MRI). The quantity of PVAT correlates with someparameters of metabolic syndrome including increased waistcircumference, hypertriglyceridemia and hyperglycemia, and with coronaryatherosclerosis. PVAT has long been known to secrete pro-inflammatoryproteins and induce inflammation of the artery wall. The long-heldunderstanding of the pathology of atherogenesis in the vascular wall wasthat it is stimulated externally, and it was suggested that PVAT playeda key role in this process.

It has recently become clear that vascular inflammation and oxidativestress has the ability to affect the biology of PVAT as the vascularwall releases mediators able to exert a paracrine effect on theneighbouring PVAT (see e.g. Margaritis et al. Circulation 2013;127(22):2209-21). This observation was in contrast to the classicaltheory according to which PVAT sends paracrine signals to the vascularwall. It is now understood that the biology of PVAT is shaped by signalsreceived from the blood vessel it surrounds, and characterisation ofPVAT can provide useful information regarding the biology and health ofthat blood vessel.

In WO2016/024128 it was demonstrated that the quantified radiodensity ofperivascular tissue (QR_(PVAT)), which is also known as and referred toherein as the fat attenuation index of perivascular tissue (FAI_(PVAT)),is positively associated with the presence of coronary artery disease(CAD) and the volume of fibrous plaque in the proximal RCA independentlyof the presence of coronary calcium. As part of the same study, thepresent inventors also showed that FAI_(PVAT) changes in a dynamic wayin response to local rupture of a culprit lesion in patients with acuteMI and can distinguish culprit from non-culprit lesions. Theseobservations supported the inventors' hypothesis that FAI_(PVAT) couldfunction as a dynamic biomarker of vascular inflammation andcardiovascular risk and offer diagnostic and prognostic informationbeyond that of traditional biomarkers, such as coronary calcium.

However, there remains an urgent need for the identification anddevelopment of functional biomarkers that will describe vascularinflammation, rather than structural and non-reversible changes in thevascular wall, and diagnostic tools to aid non-invasive detection ofvascular inflammation and enable stratification of patients who are atrisk of suffering serious cardiac events.

SUMMARY OF THE INVENTION

According to a first aspect, the present invention provides a method fordetermining the perivascular water index (PVWi) of a blood vessel,comprising (i) using data gathered from a computer tomography scan alonga length of the vessel to determine the total volume of voxels of waterwithin an attenuation window around the attenuation of water within theperivascular space a pre-determined distance from the outer wall of thevessel, and (ii) correcting the total volume of the voxels of water forthe size of the vessel by dividing the total volume of voxels of waterdetermined in step (i) by the total perivascular volume.

According to a second aspect, the present invention is directed to theuse of perivascular water index (PVWi), as defined according to themethod of the first aspect of the invention, as a functional biomarkerof vascular inflammation. According to this aspect, PVWi can be usedalone, or in combination with one or more other biomarkers, to predictall-cause or cardiac mortality risk in a patient. In particular, PVWican be used in combination with one or more of calcium index, fibrousplaque index, fat attenuation index of the perivascular adipose tissue,volumetric perivascular characterisation index, fat attenuation indexand total volume of the epicardial adipose tissue to predict all-causeor cardiac mortality risk in a patient.

According to a third aspect, the present invention provides a method forpredicting the risk of a patient suffering a cardiovascular event, saidmethod comprising:

(a) using data gathered from a computer tomography scan along a lengthof a blood vessel to determine:

(i) calcium index (Calcium-i); and/or

(ii) fibrous plaque index (FPi)

and at least one of

(iii) fat attenuation index of the perivascular adipose tissue(FAI_(PVAT)), and/or

(iv) perivascular water index (PVWi), and

with the possible addition of any of the following:

(v) volumetric perivascular characterisation index (VPCI)

(vi) total epicardial adipose tissue volume (EpAT-vol);

(vii) fat attenuation index (FAI) of epicardial adipose tissue(FAI_(EpAT)),

(b) comparing each of the values determined in (a) to a pre-determinedcut-off value or using the absolute value of each variable in order togenerate an output value that indicates the patient's risk of sufferinga cardiovascular event.

In a preferred embodiment of the method of the third aspect of theinvention, both FAI_(PVAT) and PVWi are determined in step (a) of themethod.

In one embodiment, the method according to the third aspect of theinvention further comprises determining one or more of (vi) fatattenuation index of total volume of the epicardial adipose tissue(EpAT-vol), (vii) the epicardial adipose tissue (FAI_(EpAT)), (viii) ageand (ix) gender of the patient.

In certain embodiments, the method according to the third aspect of theinvention can be used for non-invasive monitoring of aortic aneurysmsand/or carotid plaques.

DESCRIPTION OF THE DRAWINGS

The invention is described with reference to the following Figures, inwhich:

FIG. 1 provides a definition of perivascular adipose tissue (PVAT)indices. (A) Coronary CT angiography images were reconstructed in 3dimensions. The right coronary artery was tracked and the proximal 10-50mm of its course were selected on curved multiplanar reconstructionimages. The inner and outer walls of the vessel were manually optimizedand the perivascular area (up to 20 mm distal to the outer vessel wall)was analysed based on the respective tissue attenuation. A HounsfieldUnit range of −15 to +15 was used to detect perivascular water, whereasa range of −190 to −30 was applied for detection of adipose tissue. (B)The perivascular area was then split in 20 concentric cylindrical layersof 1 mm thickness and fat attenuation index (FAI), defined as the meanattenuation of adipose tissue within the pre-defined range, was thencalculated in each layer and plotted against the distance from thevessel wall. PVAT was defined as AT within a radial distance equal tothe diameter of the vessel, whereas AT in the most distal layer wasdefined as non-PVAT. The volumetric perivascular characterization index(VPCI) was then defined as the percent change from FAI_(PVAT) toFAI_(non-PVAT). Further analysis by tertiles of fibrous plaque index(FPi, fibrous plaque volume divided by the total volume of the vessel)revealed a positive association between fibrous plaque burden and FAI ofthe adipose tissue in the perivascular area. Perivascular Water Index(PVWi) was defined as the total volume of voxels of water within anattenuation window around the attenuation of water (−15 to +15 HU)within the perivascular space a pre-determined distance from the outerwall of the vessel (e.g. a radial distance equal to the diameter of thevessel) divided by the total perivascular volume.

FIG. 2 shows the correlation between perivascular fat attenuation index(FAI_(PVAT)), perivascular water index (PVWi), fibrous plaque index(FPi) and coronary calcification. FAI_(PVAT) is strongly correlated withperivascular water, supporting the hypothesis that changes in FAI_(PVAT)reflect a shift from a lipophilic to a greater aqueous phase (A). On theother hand, there was only a weak correlation between FAI_(PVAT), afunctional biomarker, and FPi, a structural wall biomarker, suggestingthat the two indices reflect a different local biology (B). Similarly,no correlation was found between FAI_(PVAT) and coronary calcium (RCAcalcium index and total Agatston score) (C, D). Taken together, thesefindings suggest that FAI_(PVAT) describes a different biology thananatomical plaque burden and it is entirely independent of the localcalcium load or the Agatston score.

FIG. 3 shows PVWi as a predictor of all-cause, cardiac and non-cardiacmortality. Receiver operating characteristic curve analysis identified acut-off of 0.10 with 57.7% sensitivity and 63.3% specificity forprediction of cardiac mortality (A). Comparison of KM curves by thelog-rank test as well as univariate Cox regression analysis showed thathigh PVWi values (0.10) are associated with a significantly higher riskof all-cause (B) and cardiac mortality (C) but not non-cardiac mortality(p=NS). AUC: area under the curve; CI: confidence intervals; HR: hazardratio PVWi: Perivascular Water index; ROC: receiver operatingcharacteristic curve.

FIG. 4 shows the predictive value of FAI_(PVAT) and VPCI for all-causeand cardiac mortality. We first explored the predictive value of ournovel imaging indices by splitting the study population in tertilesaccording to their respective FAI_(PVAT) and VPCI values. Individuals inthe high FAI_(PVAT) group had an almost two-fold increase in their riskof death (A) and almost four times higher risk of cardiac mortalitycompared to those in the lowest tertile (B). Notably, visual assessmentof the Kaplan-Meier curves revealed a similar trend for the mid- andlow-tertile groups, suggesting the presence of a certain cut-off, abovewhich the risk of mortality significantly increases. In fact, ROC curveanalysis revealed an optimal cut-off of −70.1 HU that was able topredict cardiac death with 65.4% sensitivity and 71.9% specificity (C).By following a similar approach, an optimal cut-off of 14.5% wasidentified for VPCI as a predictor of cardiac mortality (D).Interestingly, high VPCI values (14.5%) were associated with a higherrisk of cardiac-related (F) but not all-cause mortality (E). AUC: areaunder the curve; CI: confidence intervals; FAI_(PVAT): fat attenuationindex of perivascular adipose tissue; HR: hazard ratio; ROC: receiveroperating characteristic curve, VPCI: volumetric perivascularcharacterisation index.

FIG. 5 shows the predictive value of high FAI_(PVAT) (≥−70.1 HU) forall-cause, cardiac and non-cardiac mortality. In univariate Coxregression analysis, high FAI_(PVAT) was linked to a two-fold increasein the risk of all-cause mortality (A) and a more than five-foldincrease in the risk of cardiac death (B) compared to the low FAI_(PVAT)group. More importantly, FAI_(PVAT) remained predictive of bothall-cause and cardiac mortality in multivariable cox-regression (PanelC, where HR: hazard ratio from cox regression (for FAI_(PVAT)<−70.1 HUvs≥−70 HU)). *adjusted for age, gender, hypertension,hypercholesterolemia, diabetes mellitus, active smoker status,medications (antiplatelets and statins), presence of coronary arterydisease, calcium index, Agatston score (≥400 vs<400) and type of CTscanner). Interestingly, the predictive value of FAI_(PVAT) appears tobe specific for cardiac rather than non-cardiac mortality, suggestingthat the new biomarker describes a cardiac-specific biology and that itprovides additional information, beyond that of traditional risk factorsand biomarkers used in cardiac risk stratification. CAD: Coronary ArteryDisease; FAI_(PVAT): Fat Attenuation Index of Perivascular AdiposeTissue; HU: Hounsfield units.

FIG. 6 shows mortality and cardiac risk-stratification based on theOxScore. A novel predictive model was constructed based on four imagingindices that were shown to be strong and independent predictors ofall-cause and cardiac mortality in multivariable Cox regression analysis(A). These were FAI_(PVAT), a novel marker of coronary inflammation,fibrous plaque index, a biomarker of soft plaques, calcium index, animaging index of local calcium deposition in the proximal right coronaryartery and finally epicardial adipose tissue (EpAT) volume, anestablished index of epicardial/visceral adiposity. Based on a logisticregression model, the individual probabilities of all-cause/cardiacmortality were calculated and the study population was subsequentlyreclassified into risk groups as follows: for all-cause mortality:OxScore_(high): ≥10%, OxScore_(mid): 5-10%, OxScore_(low): <5% (B) andfor cardiac mortality: OxScore_(high): ≥3% versus OxScore_(low): <3%(D). Patients in the OxScore_(high) group for all-cause mortality had analmost eight times higher risk of death during follow-up compared to theOxScore_(low) group (C), whereas those in the high-risk group forcardiac-specific mortality, were more than 22 times more likely to dieof cardiac causes compared to the low risk group (E). FAI_(PVAT): FatAttenuation Index of Perivascular Adipose Tissue; FPi: Fibrous Plaqueindex; HR: Hazard Ratio; HU: Hounsfield units.

FIG. 7 compares OxScore against traditional risk factors and cardiac CTmeasurements. In order to examine the predictive value of OxScore beyondage, gender, traditional cardiovascular risk factors and standardinterpretation of a cardiac CT scan (presence of new or previously knowncoronary artery disease or high Agatston score≥400), two differentmodels were constructed as follows. Model 1 included age, gender,hypertension (HTN), hypercholesterolemia, diabetes mellitus, activesmoker status, presence of coronary artery disease (CAD), Agatstoncoronary calcium score (CCS), while Model 2 was created by adding theOxScore variables into Model 1. Interestingly, addition of the OxScorevariables into the model significantly improved the predictive value forboth all-cause and cardiac specific mortality (Δ[AUC]=0.031, P<0.05 forall-cause and Δ[AUC]=0.10, P<0.01 for cardiac mortality) (A, C).Furthermore, addition of the OxScore improved risk classificationcompared to the standard model, as shown by an NRI index of 7.6% and11.3% for all-cause and cardiac mortality respectively (B, D). Notably,OxScore appears to predominantly improve reclassification of non-events,suggesting a potential value for this novel risk scoring system inidentifying high-risk individuals among those with already presenttraditional cardiovascular risk factors. AUC: area under the curve; CAD:coronary artery disease; CT: computed tomography; FPi: Fibrous Plaqueindex; NRI: net reclassification improvement. NS: non significant.

FIG. 8 shows how PVWi (perivascular water index) is calculated arounddifferent vessels. PVWi is calculated along the right coronary artery(RCA) (A), left anterior descending artery (LAD) (B), left circumflexartery (LCx) (C), aorta (D) and the common carotid artery (E),respectively.

FIG. 9 shows the generation of a novel risk score (Cardiac Risk Score orCaRi Score) by adding the beta coefficients for FAI_(PVAT), FPi andCalcium-i as estimated in an adjusted Cox regression model for cardiacmortality, resulting in a score that ranged from 1.23 to 11.52, with amean of 5.56 and standard deviation of 1.45 (A). Following multivariableadjustment for age, gender, risk factors and presence of coronary arterydisease, CaRi score was identified as a strong and independent predictorof both all-cause and cardiac mortality (adj. HR[95% CI]: 1.46[1.28-1.65] and 2.71 [1.99-3.69] per 1 unit increments for all-cause andcardiac mortality respectively, P<0.001 for both). Indeed, there was agraded relationship between CaRi score and all-cause/cardiac mortality,with higher CaRi values corresponding to a higher risk of mortality (B,C). (Calcium-i: calcium index; CI: Confidence Interval; FAI_(PVAT): FatAttenuation Index of Perivascular Adipose Tissue; FPi: fibrous plaqueindex; HR: hazard ratio; HU: Hounsfield Units).

DETAILED DESCRIPTION OF THE INVENTION

The present inventors have developed a new functional biomarker ofvascular inflammation which can be used alone or in combination withother known structural and/or functional biomarkers of vascularinflammation, to predict, with a high degree of accuracy, the risk of acoronary event occurring.

The new functional biomarker of vascular inflammation is a novel indexthat has been identified by the present inventors, which is referred toherein as “Perivascular Water Index (PVWi)”. PVWi is defined as thevolume of the voxels within a window above and below the attenuation ofwater that corresponds to the water content around the inflamed vessel.This biomarker can be used to detect vascular inflammation and/orpredict risk of a coronary event occurring on its own, or in combinationwith other functional or structural biomarkers, as described in detailbelow.

Therefore, according to a first aspect, the present invention provides amethod for determining the perivascular water index (PVWi) of a bloodvessel, comprising:

-   -   (i) using data gathered from a computer tomography scan along a        length of the vessel to determine the total volume of voxels of        water within an attenuation window around the attenuation of        water within the perivascular space a pre-determined distance        from the outer wall of the vessel, and    -   (ii) correcting the total volume of the voxels of water for the        volume of the vessel by dividing the total volume of voxels of        water determined in step (i) by the total perivascular volume.

The total perivascular volume is defined as the total volume of voxelswithin a radial distance away from the vascular wall that isrepresentative of the vessel dimensions. For example, the distance maybe the diameter of the vessel, or any other aspect that describes thedimensions of the vessel (such as (vessel diameter)/2 or (vesseldiameter)×3, or any other subdivision or multiple of a dimension of thevessel.

As used herein, the term “computer tomography scan” refers to a scangenerated using computer-processed x-rays to produce tomographic imagesof specific areas of the scanned perivascular region. The term “computedtomography scan” is synonymous with the terms CT scan and CAT scan.Preferably the CT scan of a blood vessel, or a section thereof, iscarried out using routine methods and commercially availableinstruments.

As used therein, the term “perivascular” refers to the space thatsurrounds a blood vessel. The term “perivascular tissue” refers to thetissue that surrounds a blood vessel, and may include perivascularadipose tissue (PVAT). The terms “perivascular tissue” and “perivascularspace” are used interchangeably herein.

The term “radiodensity” is synonymous with the term “attenuation” andthe two terms can be used interchangeably, although the term“attenuation” is preferred.

Attenuation, which is measured in Hounsfield units (HU), is a measure ofthe relative inability of X-rays to pass through material. Measurementof attenuation values allows tissue types to be distinguished in CT onthe basis of their different radio-opacities. Fat is not veryradiodense, and it typically measures between −190 and −30 HU whilemuscle, blood and bone measure between +10 and +40, between +30 and +45,and between +700 and +3000 HU respectively.

In the context of the present invention, an “average” value isunderstood to mean a central or typical value, and it can be calculatedfrom a sample of measured values using formulas that are widely knownand appreciated in the art. Preferably, the average is calculated as thearithmetic mean of the sample of attenuation values, but it can also becalculated as the geometric mean, the harmonic mean, the median or themode of a set of collected attenuation values. The average value may becalculated by reference to data collected from all voxels within aconcentric tissue layer or by reference to a selected population ofvoxels within the concentric tissue layer, for example water- or adiposetissue-containing voxels.

The term “voxel” has its usual meaning in the art and is a contractionof the words “volume” and “element” referring to each of an array ofdiscrete elements of volume that constitute a notional three-dimensionalspace.

The term “vascular inflammation” has its usual meaning in the art, andrefers to a progressive inflammatory condition characterized by thevascular infiltration by white blood cells, build-up of scleroticplaques within vascular walls, and in particular, arterial walls.Vascular inflammation is a key process for the initiation andprogression of atherosclerosis and vascular disease.

The phrase “conditions associated with vascular inflammation” includesany disease where vascular inflammation is known to play a key role inpathogenesis, such as coronary artery disease, aortic and other vascularaneurysms, carotid plaques, peripheral arterial disease.

In a preferred embodiment of this aspect of the invention, theattenuation window around the attenuation of water is from −30 to +30Hounsfield units (HU), and more preferably from −15 to +15 HU.

In a preferred embodiment of this aspect of the invention, thepre-determined distance from the outer wall of the vessel referred to instep (i) can be any one of the following three distances:

1. A distance equal to the diameter or radius of the underlying vessel.2. A distance that is representative of a dimension of the underlyingvessel (e.g. any subdivision or multiple of the radius or diameter ofthe vessel).3. A standard predetermined distance that is not equal to or related tothe diameter of the underlying vessel (e.g. 5 mm).

Preferably, the blood vessel is a coronary blood vessel, such as theaorta. In a preferred embodiment the data is gathered from acomputerised tomography scan along a length of the right coronaryartery, left anterior descending artery, left circumflex artery, aorta,carotid arteries or femoral arteries. More preferably, the data isgathered from a computerised tomography scan along a 4 cm length,starting 1 cm distally to the origin of the right coronary artery.

For the avoidance of doubt, the methods of the invention utilise CT scandata that has been obtained in vivo, by scanning a living body, but theclaimed methods are not practised on the living human or animal body.

PVWi has utility as a functional biomarker of vascular inflammation, andin particular can be used to predict cardiac mortality risk in apatient.

Therefore, a second aspect of the present invention is directed to theuse of perivascular water index (PVWi), as defined according to themethod of the first aspect of the invention, as a functional biomarkerof vascular inflammation.

PVWi may be used alone, or may be used in combination with additionalfunctional and/or structural biomarkers. Preferably, the structuralbiomarkers include one or more of calcium index (Calcium-i), fibrousplaque index (FPi) or total epicardial adipose tissue volume (EpAT-vol).Preferably, the additional functional biomarkers of vascularinflammation include one or more of the fat attenuation index of theperivascular adipose tissue (FAI_(PVAT)), volumetric perivascularcharacterisation index (VPCI) and epicardial adipose tissue FatAttenuation Index (FAI_(EpAT)).

VPCI is defined as the difference between the quantified attenuation (orradiodensity) of perivascular adipose tissue (FAI_(PVAT)) and thequantified attenuation (or radiodensity) of non-perivascular adiposetissue (FAI_(nPVAT)). Non-perivascular adipose tissue (nPVAT) is definedas adipose tissue that is located 2 cm or more away from the outer wallof the vessel.

The VPCI and FAI indices are defined and described in detail in thepresent inventors' earlier patent publication WO2016/024128, the entirecontents of which are incorporate by reference. In that publication FAIis referred to as the QR index (but the two are synonymous).

The terms “Fibrous Plaque Index (FPI)” and “(Fibrous) plaque” aresynonymous and are used interchangeably herein. Fibrous plaque index isdefined as the total volume of all voxels corresponding to fibroustissue within the wall of a vascular segment (e.g. between 65 and 260HU), divided by the total volume of the respective vascular segment.

Calcium index (Calcium-i) is also known in the art as “(coronary)calcification”, “calcium volume” of an artery, and these synonyms may beused interchangeably herein. Calcium-index is defined as the total ofvolume of all voxels corresponding to local calcium within the wall of avascular segment (>465 HU), divided by the total volume of therespective coronary segment.

Epicardial adipose tissue volume (EpAT-vol) refers to the total volumeof all voxels (within the pre-specified thresholds of −190 to −30 HU)corresponding to epicardial adipose tissue. Epicardial adipose tissue isdefined as any adipose tissue located between the myocardium and thepericardium. Alternatively, EpAT-vol can be indexed for differences inbody size, e.g. body surface area.

Epicardial adipose tissue Fat Attenuation Index (FAI_(EpAT)) refers tothe average attenuation of all voxels corresponding to EpAT (within thepre-specified threshold of −190 to −30 HU).

The terms “patient” and “subject” are used interchangeably herein. Theseterms can refer to any animal (e.g. mammal), including, but not limitedto, humans, non-human primates, canines, felines, rodents and the like.Preferably the patient or subject is a human. The patient may be anindividual who has been diagnosed as suffering from a conditionassociated with vascular inflammation or who is suspected of, or at riskof, suffering from a condition vascular inflammation, in particularvascular inflammation affecting the coronary vessels.

A third aspect of the present invention is directed to a novel methodfor predicting cardiac events, including cardiac death. The method isbased on a novel scoring system that has been developed by the presentinventors and is referred to herein as the “OxScore (Oxford integratedcoronary CT Score)”.

The OxScore is based on the observation that vascular inflammation willincrease the aqueous phase of the tissue surrounding the inflamedvessel, and this is identified by combining measurements of the volumeof this aqueous phase with a shift of the overall attenuation of thetissues surrounding the vessel. When this approach was combined withinformation about the structure of the vascular wall andepicardial/visceral obesity, the inventors generated a novel score thathas been found to be superior to any other imaging biomarker inpredicting all-cause and cardiac mortality. This represents a new riskscore that predicts mortality due to any or cardiac-specific causes. Themethod is based on a combination of computed tomography (CT) biomarkersthat track vascular inflammation and vulnerable atherosclerotic plaquesthrough volumetric and qualitative changes of the attenuation (orradiodensity) of vascular and perivascular tissues. OxScore provides aunified score that strongly predicts cardiac events and cardiacmortality, and significantly more strongly than any of these indices inisolation.

Uniquely, the OxScore is the only method where coronary artery diseaseis evaluated by quantification of changes both in the vessel wall, (thelocation of coronary artery plaques), and in the surrounding tissue(where changes reflect the inflammatory status and risk of the plaque).No other similar approaches have been described previously, and themethod of this aspect of the invention is the first to monitor changesin perivascular tissue attenuation and volumetric characteristics toquantify vascular inflammation and cardiovascular risk.

Accordingly, a third aspect of the invention provides a method forpredicting the risk of a patient suffering a cardiovascular event, saidmethod comprising:

-   -   (a) using data gathered from a computer tomography (CT) scan        along a length of a blood vessel to determine:    -   (i) calcium index (Calcium-i); and/or    -   (ii) fibrous plaque index (FPi) and at least one of    -   (iii) fat attenuation index of the perivascular adipose tissue        (FAI_(PVAT)), and/or    -   (iv) perivascular water index (PVWi), and with the possible        addition of one or more of the following:    -   (v) volumetric perivascular characterisation index (VPCI)    -   (vi) total epicardial adipose tissue volume (EpAT-vol);    -   (vii) fat attenuation index (FAI) of epicardial adipose tissue        (FAI_(EpAT)),    -   (b) comparing each of the values determined in (a) to a        pre-determined cut-off value or using the absolute value of each        variable in order to generate an output value that indicates the        patient's risk of suffering a cardiovascular event.

In one embodiment, FAI_(PVAT) is determined in step (a). In anotherembodiment PVWi is determined in step (a). In a preferred embodiment,both FAI_(PVAT) and PVWi are determined in step (a) of the method. In afurther embodiment FAI_(EpAT) is determined in step (a). In anotherembodiment, FAI_(PVAT) and FAI_(EpAT) or PVWi and FAI_(EpAT) aredetermined in step (a). In a further embodiment, all of FAI_(PVAT), PVWiand FAI_(EpAT) are determined in step (a).

The indices FAI_(PVAT), PVWi, VPCI, Calcium-i and FPi, FAI_(EpAT),EpAT-vol are as defined herein above.

Preferably, the data is gathered from a CT scan along a length of theright coronary artery, left anterior descending artery, left circumflexartery, aorta, carotid arteries or femoral arteries.

In a preferred embodiment, the data is gathered from a computerisedtomography scan along a 4 cm length, starting 1 cm distally to theorigin of the right coronary artery.

In a preferred embodiment, the data is gathered from a computerisedtomography scan along a length of the aorta.

Preferably, the cut-off points for each of (i) to (vii) are derived fromROC curves. Based on the ROC curves, an optimal cut-off point isselected that yields the optimal sensitivity and specificity for theprediction of the desired endpoint, e.g. cardiac mortality (see, forexample, FIGS. 3, 4 ).

In one embodiment, the method according to the third aspect of theinvention further comprises the age and/or gender of the patient as wellas other established cardiovascular risk factors, such as coronarycalcium (measured on non-contrast CT scans, e.g. Agatston score),hypertension, hyperlipidemia/hypercholesterolemia, diabetes mellitus,presence of coronary artery disease, smoking, family history of heartdisease etc.

In one embodiment, the output value that corresponds to or indicatesrisk of a cardiac event is a continuous single value function. Forexample, the absolute values for each variable can be integrated intoone single formula along with calculated coefficients to yield anindividualised risk prediction/probability.

In an alternative embodiment, the unstandardized beta coefficients ofFAI_(PVAT), Calcium-i and FPi, as calculated in a Cox or logisticregression model with cardiac or all-cause mortality as the dependentvariable/outcome of interest, can be combined (as shown in FIG. 9 ) togenerate an alternative risk score (e.g. Cardiac Risk score, or CaRiscore). An example of the CaRi-based mortality risk score is presentedin FIG. 9 .

An example for a specific cohort, wherein constants are determined onthe basis of the background of the patient cohort, is provided below(also see FIG. 6 ).

An example of a formula used to calculate the OxScore probability ofall-cause/cardiac mortality is provided below.

OxScore=Risk (probability) of event (%)=100*10^(y)/(1+10^(y))

and y=c+a*FAI_(PVAT) +b*FPi+d*Calcium-I+e*EpATvol

where, a, b, d, e=beta coefficients and c=constant calculated onlogistic regression with FAI_(PVAT), FPi, Calcium-I, EpAT volume as theindependent variables and all-cause, cardiac mortality or cardiac eventsas the dependent variable. Alternatively, coefficients can be calculatedfrom Cox regression hazard models.

In one embodiment, both PVWi and FAI_(PVAT) are included in the samemodel.

In an alternative embodiment, PVWi, FAI_(PVAT), VPCI, FPi, Calcium-i,EpAT volume, FAI_(EpAT), age and gender are all included in the samemodel. An example of the OxScore-based mortality risk is provided below:

OxScore=Risk (probability) of event(%)=100*10^(y)/(1+10^(y))

where,

y=c+a*FAI_(PVAT) +b*FPi+d*Calcium-I+e*EpATvol+f*FAI_(EpAT)+g*PVWi+h*VPCI+k*age+l*gender

where, a, b, d, e, f, g, h, k, I=beta coefficients and c=constantcalculated on logistic regression with FAI_(PVAT), FPi, Calcium-I, EpATvolume, FAI_(EpAT), PVWi, VPCI, age and gender (as categorical, e.g.1=male, 0=female) as the independent variables and all-cause, cardiacmortality or cardiac events as the dependent variable. Alternatively,coefficients can be calculated from Cox regression hazard models.

In an alternative embodiment, the output value that corresponds to orindicates risk of a cardiac event is a value that falls within one ofthree discrete brackets corresponding to low, medium and high risk ofsuffering a cardiovascular event (see FIG. 6 ).

Preferably, the patient has been diagnosed with vascular inflammation,or a condition known to be associated with vascular inflammation.

The OxScore and the individual indices on which it is based are usefulfor predicting cardiac death and cardiac events and so the method of theinvention can be used to stratify patients according to their risk ofcardiac mortality.

OxScore method can be used as an adjunctive tool in routine clinical CTangiograms to identify patients at high risk of cardiac events andmortality including a sensitive and specific screening tool in peoplewho are apparently healthy and low-risk according to the traditionalinterpretation of their scans. The OxScore method has utility both inprimary prevention (healthy population with no diagnosis of heartdisease yet) and secondary prevention (patients with a diagnosis ofcoronary artery disease), to identify an individual's risk status beyondtraditional risk factors, to guide pharmacological treatment decisions,and to monitor response to appropriate medical treatments. To this end,OxScore could be measured automatically using dedicated softwareproviding a rapid, non-invasive estimation of an individual's riskstatus and guide clinical decision making.

Accordingly, the patient may be an individual who has been diagnosed assuffering from a condition associated with vascular inflammation, or whois suspected of, or at risk of, suffering from a condition vascularinflammation, in particular vascular inflammation affecting the coronaryvessels. Alternatively the patient may be a healthy individual who hasnot been diagnosed as suffering from a condition associated withvascular inflammation, and/or who is not known to be at risk ofsuffering from a condition vascular inflammation.

Despite the popularity of coronary CT angiography as a diagnostic methodfor coronary artery disease, coronary calcium score (CCS) remains theonly CT-based method of cardiovascular risk stratification, with anestablished role in clinical practice. However, CCS is only a structuralbiomarker and only identifies one component of the coronary plaque(calcification), which does not change with the inflammatory status ofthe vessels and does not improve following appropriate medicalmanagement. CCS primarily reflects ageing and it even predictsnon-cardiac events (i.e. it is sensitive but not specific for cardiacevents). More importantly, no method has been described with an abilityto track subclinical changes in coronary inflammation on routineeveryday CT angiography.

The OxScore method combines “functional” biomarkers of vascularinflammation (PVWi, VPCI, FAI_(PVAT) and FAI_(EpAT)) with indices ofstructural vascular disease (calcium-i and FPi) and visceral adiposity(EpAT-vol), to generate an integrated scoring system that significantlyadvances both the diagnostic and prognostic value of routine clinical CTangiography.

Importantly, the method of the invention is non-invasive and is based onthe analysis of conventional CT images; it does not require anyadditional image acquisition.

Certain embodiments of the method of this aspect of the invention can beused for non-invasive monitoring of aortic aneurysms and/or carotidplaques. However, EpAT volume and FAI cannot be applied to othervessels.

The OxScore method may be utilised in a method of treating a conditionassociated with vascular inflammation in a patient.

According to this aspect of the invention, a method of treating acondition associated with vascular inflammation in a patient comprisescarrying out the method according to the third aspect of the inventionas described above, and, if the outcome of said method indicates thatthe patient is at risk of suffering a cardiac event, administering asuitable therapy and/or surgical intervention to said patient.

The invention is further described with reference to the followingnon-limiting example:

Example Methods Patients

In this prospective study, a cohort of 1993 subjects was recruitedprospectively between 2005 and 2009, following a clinically indicatedCTA performed at the Erlangen University Hospital (Erlangen, Germany). Atotal of 1872 subjects had analysable CTA scans and were included in thestudy. The vast majority of the scans were performed for exclusion ofcoronary artery disease (CAD) (91.7%). Most of the patients hadpresented with atypical symptoms (85.3%) and less than half had ahistory of chest pain (43.4%). A minority of the scans (3.8%) wasperformed in patients with previously known CAD to evaluate possibledisease progression (3.7%) or the patency status of a vascular graft(0.1%). Following the baseline CT scan, only a small proportion of thecohort was diagnosed with obstructive CAD (21.6%). The patientdemographics and clinical characteristics of the studied population aresummarized in Table 1.

TABLE 1 Cohort demographics and clinical characteristics of the studypopulation Subjects screened (n) 1993 Subjects included in the study (n)1872 Age (years) 60.1 ± 11.9 Male gender (%) 62.9 Risk factors*Hypertension (%) 61.9 Hypercholesterolemia (%) 54.7 Diabetes Mellitus(%) 12.4 Active/past smoking (%) 12.8/21.4 Family history of heartdisease (%) 25.6 Medications at baseline** Antiplatelets(aspirin/clopidogrel) (%) 37.6 Statins (%) 34.6 ACEi or ARBs (%) 43.1Beta-blockers (%) 44.8 CT scan CT scanner type 64-slice (%) 18.164-slice DSCT (%) 79.2 128-slice DSCT (%) 2.7 Tube voltage 100 keV (%)22.2 120 keV (%) 77.8 Total Agatston score^(†) <400 (%) 85.3% ≥400 (%)14.7% Follow-up Duration in months (median [range]) 72 [51-109] Totalmortality n (%) 114 (6.1) Confirmed cardiac mortality n (%) 26 (1.4)Confirmed non-cardiac mortality n (%) 72 (3.8) Unknown cause of death n(%) 16 (0.9) (DS)CT: (dual source) computerised tomography; valuespresented as mean ± SD unless otherwise stated; maximum missingness:*9.2%, **13.9%, ^(†)24.4%

Study Design

This is a prospective cohort study of subjects who underwent CTA between2005 and 2009. Follow-up was performed at an average interval of77.0±14.2 months (range: 51-109 months) after the baseline scan. Datawere collected on the primary endpoints of all-cause and cardiacmortality. Significant and independent predictors of all-cause andcardiac-specific mortality were then integrated into a single model, togenerate a novel CTA-based method of cardiovascular risk stratification.

Definitions: Cardiac and non-cardiac mortalities were defined accordingto the “2014 ACC/AHA Key Data Elements and Definitions forCardiovascular Endpoint Events in Clinical Trials” (Hicks et al., 2015)taking also into account the recommendations of the Academic ResearchConsortium (Cutlip et al., 2007). Cardiac death was defined as any deathdue to proximate cardiac causes (e.g. myocardial infarction, low-outputheart failure, fatal arrhythmia). Deaths fulfilling the criteria ofsudden cardiac death were also included in this group. Any death notcovered by the previous definition, such as death caused by malignancy,accident, infection, sepsis, renal failure, suicide or other non-cardiacvascular causes such as stroke or pulmonary embolism was classified asnon-cardiac. A subgroup of deaths where the data on the cause of deathcould not be collected with certainty were classified as “deaths ofunknown cause”. CAD was defined as the presence of obstructive diseaseseen on CTA (≥50% stenosis) or previous, known history of CAD.

CT Angiography

All participants underwent coronary CTA and in most of the scans (75.6%)additional non-contrast images were acquired for the purpose ofmeasuring Agatston coronary calcium score. The vast majority of thescans (79.2%) were performed in a dual-source 64-slice scanner, whereasthe rest were done either in a 64-slice (18.1%) or dual-source 128-slicescanner (2.7%). Heart rate was optimised using intravenous injection ofbeta-blockers and sublingual glyceryl-trinitrate (800 ug) was alsoadministered to achieve maximum coronary vasodilatation. CTA wasperformed following intravenous injection of 95 ml of iodine basedcontrast medium at a flow rate of 6 mL/sec (tube energy of 80, 100 or120 kV). Prospective image acquisition was used by ECG-gating at 75% ofcardiac cycle (with 100 msec padding for optimal imaging of the rightcoronary artery if required).

Analysis of CT angiograms: The reconstructed images were transferred toa processing system and analysis workstation (Aquarius Workstation®V.4.4.11 and 4.4.12, TeraRecon Inc., Foster City, Calif., USA). Vascularand perivascular tissue components were characterized according topreviously described and validated attenuation maps (Obaid et al.,2013). Since our attenuation-based method for characterization ofvascular and perivascular tissue has only been validated sufficiently inCT angiograms performed at a tube voltage of either 100 or 120 kV (Obaidet al., 2013; Okayama et al., 2012) scans done at 80 kV (n=14) wereexcluded from our study. Additional exclusion criteria were the presenceof significant artefacts that made the analysis not possible (e.g.blooming or step artefacts) or poor overall image quality that precludeda reliable assessment of the coronary anatomy in the proximal rightcoronary artery (RCA) or the total epicardial adipose tissue (EpAT).Four researchers blinded to patient demographics and outcomes workedindependently for the analysis of the vascular wall perivascular tissue(two researchers) and EpAT (two researchers).

The inter/intra observer variability for these analyses is presented insupplementary Table 2.

TABLE 2 Inter- and intra-observer variability Inter-observerIntra-observer Variable CV (%) CV (%) Vessel diameter 1.97 0.91 Fibrousplaque index 2.80 1.35 Perivascular Water index 1.78 1.68 FAI_(PVAT)0.53 0.18 EpAT volume 3.46 2.67 CV: coefficient of variation; EpAT:Epicardial Adipose Tissue; FAI: Fat Attenuation Index; PVAT:Perivascular Adipose Tissue

Agatston coronary calcium score: The Agatston coronary calcium score wascalculated on the non-contrast images using standard analysis tools(Aquarius Workstation® V.4.4.11 and 4.4.12, TeraRecon Inc., Foster City,Calif., USA).

Adipose tissue analysis: Adipose tissue was defined as all voxels withattenuation within a pre-specified window of −190 to −30 HounsfieldUnits (HU). The total EpAT volume was assessed in a semi-automatedmanner by tracking the contour of the pericardium from the level of thepulmonary artery bifurcation to the apex of the heart at the most caudalend. Voxel attenuation histograms were plotted and FAI was defined asthe mean attenuation of all voxels within the pre-specified range of−190 to −30 HU (Tamarappoo et al., 2010; Hell et al., 2016). To adjustfor differences in mean attenuation between scans done at different tubevoltages, adipose tissue FAI for scans done at 100 kV was divided by aconversion factor of 1.11485 to be comparable to scans performed at 120kV, as previously described (Okayama, 2012 et al.)

Coronary wall analysis: The vascular segment of interest was identifiedon 3-dimensional curved multiplanar reconstruction images. For thepurposes of our study, analysis was restricted to the proximal 10-50 mmof the RCA. The benefits of this method have been described in ourprevious work (Antonopoulos et al., in review). In short, the absence oflarge branches in this segment allows a clear anatomical separation ofPVAT and non-perivascular adipose tissue (non-PVAT) compartments, whilethe proximal 10 mm of the RCA are excluded due to their proximity to theaorta. The lumen as well as the inner and outer wall border were trackedin an automated way with additional manual optimization and validated HUthresholds were applied for characterization of vascular wall components(65 to 260 HU for fibrous plaque and >465 HU for calcification) (Obaidet al., 2013). The Fibrous Plaque index (FPi) and Calcium-index(Calcium-i) were defined by dividing the total volume of fibrous plaqueor coronary calcium by the volume of the respective vessel segment.

Perivascular tissue analysis: Following tracking of the segment ofinterest in the proximal RCA (i.e. the proximal 4 cm of the RCA starting1 cm away from the RCA ostium), the perivascular area was segmented into20 concentric cylindrical layers of 1 mm thickness each. Based on ourprevious work (Antonopoulos et al., in review), we defined PVAT asadipose tissue located within a radial distance equal to the diameter ofthe respective vessel extending from the outer vessel wall. This isbased on a biological definition of PVAT derived from adipose tissuebiopsies from the perivascular area which demonstrated a differentadipose tissue phenotype (with smaller adipocytes, lower expression ofadipogenic genes and less lipophilic/greater aqueous phase) close to thevessel compared with adipose tissue 2 cm away from the vascular wall. Inaddition, mean attenuation of PVAT has been shown to be independent oflumen attenuation (Antonopoulos et al., in review), thus avoiding apartial volume effect (Hell et al., 2016) Voxel attenuation histogramswere plotted and the mean attenuation of all voxels characterized asadipose tissue within this volume was defined as FAI_(PVAT). Next, therespective FAI index was calculated for adipose tissue in each of the 20concentric cylindrical layers and was plotted against the radialdistance from the outer vessel wall. On the other hand, FAI_(non-PVAT)was defined as the FAI value of adipose tissue in the most distalcylindrical layer (2 cm away from the vascular wall). In order todescribe the change in adipose tissue attenuation between PVAT andnon-PVAT, the volumetric perivascular characterization index (VPCI) wascreated and was defined as the % change from FAI_(PVAT) toFAI_(non-PVAT) [VPCI=100×(FAI_(PVAT)−FAI_(non-PVAT))/|FAI_(PVAT)|] (FIG.1 ). In our previous work (Antonopoulos et al, under review) we foundthat VPCI correlates with the presence of “soft atheroscleroticplaques”, as defined by using the standard plaque analysis methodology(Obaid et al., 2013).

Based on our working hypothesis that vascular inflammation impairs thedifferentiation of adipocytes and shifts the balance towards a greateraqueous than lipophilic phase, we then tracked the volume of the aqueousphase in the perivascular area by applying an attenuation window of −15to +15 HU. The total volume of all voxels within this range was thendivided by the total perivascular volume to define the PerivascularWater Index (PVWi).

Statistical Analysis

All continuous variables were tested for normal distribution using theKolmogorov-Smirnov test. Mean values between two independent groups werecompared by unpaired Student's t-test or Mann-whitney U test asappropriate, while one-way ANOVA or Kruskal-Wallis test was used forcomparisons between three or more groups. Correlations betweencontinuous variables were assessed with Pearson's r or Spearman's rhocoefficient, as appropriate.

The predictive value of the variables of interest for the primaryendpoints of all-cause and cardiac mortality was first tested inunivariate Cox regression analysis, and Kaplan-Meier curves weregenerated and compared by the log-rank test. Based on receiver operatingcurve (ROC) analysis, an appropriate cut-off was identified for PVWi,FAI_(PVAT) and VPCI and the imaging biomarkers were then tested in amultivariable Cox regression model, adjusting for age, gender,traditional risk factors, clinically relevant medication, imageacquisition parameters, presence of CAD and Agatston score. Imagingbiomarkers derived from a standard coronary CTA that were found to beindependent predictors of all-cause/cardiac mortality were selected togenerate a novel predictive model for cardiovascular riskstratification. Based on bivariate logistic regression, an individualprobability (risk) was calculated for each study participant and thestudy population was stratified according to the respective risk forall-cause or cardiac mortality. Then the additional predictive value ofour set of biomarkers (“OxScore”) was compared against a standard modelcomposed of age, gender, cardiovascular risk factors, CAD and Agatstonscore (≥400 vs <400) (Model 1). The predictive value of Model 1 wascompared against Model 2 (Model 1+OxScore variables) by the WaldChi-square test and the C-statistic (Area Under the Curve) of therespective receiver operating characteristic (ROC) curves both forcardiac and all-cause mortality. Risk restratification of the studypopulation was quantified by the Net Reclassification Improvement index.

Results Patients and Outcomes

Among the 1993 subjects who underwent CTA, 121 scans were excluded (107due to poor image quality or presence of artefacts, 14 scans performedat 80 kV), leaving 1872 suitable for analysis. The subjects werefollowed up for an average of 77±14.2 months after the baseline scan[range from 51 to 109 months]. During the follow up, there were 114deaths (26 confirmed cardiac (1.4%), 72 confirmed non-cardiac (3.8%) and16 deaths of unknown cause (0.9%)).

Validation of the Method

In accordance with our previous findings, in this study we observed astrong correlation between FAI_(PVAT) and PVWi (FIG. 2A). FAI_(PVAT) wasonly weakly correlated with FPi (r=0.179, P<0.001, FIG. 2B) but therewas no significant correlation between FAI_(PVAT) and either RCAcalcium-i (P=0.18, FIG. 2C) or total Agatston score (P=0.869, FIG. 2D).These findings confirmed that FAI_(PVAT) describes a different (althoughindirectly related) biology, distinct from anatomical plaque burden andis entirely independent of the presence of coronary arterycalcification. However, since PVWi and FAI_(PVAT) describe a similarbiology they were not included in the same multivariable model.

Prediction of Mortality

The predictive value of PVWi was first tested in ROC analysis thatidentified a cut-off of 0.10 with 57.7% sensitivity and 63.3%specificity for prediction of cardiac mortality (FIG. 3A). Comparison ofKM curves by the log-rank test as well as univariate Cox regressionanalysis showed that high PVWi values (0.10) are associated with asignificantly higher risk of all-cause (FIG. 3B) and cardiac mortality(FIG. 3C) but not non-cardiac mortality (p=NS, FIG. 3D).

Next, the predictive value of FAI_(PVAT) and VPCI were tested inunivariate Cox regression hazard models. Individuals in the highesttertile of FAI_(PVAT) had a significantly higher risk of both all-causeand cardiac mortality, compared to those in the low tertile (FIG. 4A-B).In ROC curve analysis for cardiac mortality, a cut-off of −70.1 HU wasidentified as the value yielding the optimal sensitivity and specificityfor FAI_(PVAT) as a predictor of cardiac death (65.4% and 71.9%respectively) (FIG. 4C). By following a similar approach, an optimalcut-off of 14.5% was identified for VPCI as a predictor of cardiacmortality (FIG. 4D). Interestingly, high VPCI values (≥14.5%) wereassociated with a higher risk of cardiac-related but not all-causemortality (FIG. 4E-F).

In univariate Cox-regression analysis (Table 3), both high FAI_(PVAT)values (≥−70.1 HU) and FAI_(EpAT) were found to be significantpredictors of all-cause and cardiac mortality but not non-cardiac death,with higher adipose tissue attenuation in both depots linked to a higherall-cause or cardiac-specific mortality risk (FIG. 5A-B). High VPCIvalues 14.5%) were also associated with a two-fold increase in the riskof cardiac death, but not all-cause or non-cardiac mortality. Fibrousplaque burden (measured by FPi) was a significant predictor of all-causeand cardiac deaths, but not non-cardiac mortality. Epicardial obesity(measured by EpAT-vol) and coronary calcification (Calcium-i_(RCA)) werealso found to be significant predictors of all three endpoints.

TABLE 3 Univariate Cox regression for prediction of all-cause, cardiacand non-cardiac mortality CTA-derived All-cause mortality Cardiacmortality Non-cardiac mortality indices HR[95% CI], p value HR[95% CI],p value HR[95% CI], p value PVWi (≥0.1 1.57 [1.09-2.27], 2.35[1.08-5.11], 1.57 [0.99-2.49], versus <0.1) p = 0.016 p = 0.032 p = 0.06FAI_(PVAT) (≥−70.1 2.11 [1.445-3.082], 5.206 [2.298-11.975], 1.516[0.923-2.491], HU vs <−70.1 HU) p < 0.001 p < 0.001 p = 0.100 FAI_(EpaT)(per 1 1.043 [1.012-1.075], 1.081 [1.016-1.152], 1.035 [0.996-1.075], HUincrease) p = 0.006 p = 0.014 p = 0.08 Calcium-i (>0 3.606[2.493-5.217], 5.644 [2.59-12.3], 3.093 [1.934-4.945], vs 0) p < 0.001 p< 0.001 p < 0.001 FPi (per 0.01 1.037 [1.007-1.067], 1.115 [1.064-1.17],1.011 [0.973-1.051], unit increase) p = 0.014 p < 0.001 p = 0.56 EpATvolume (per 1.006 [1.003-1.009], 1.007 [1.001-1.012], 1.005[1.001-1.009], cm³ increase) p < 0.001 p = 0.021 p = 0.008 VPCI (≥14.5%1.250 [0.860-1.817], 2.215 [1.015-4.832], 1.037 [0.642-1.676], vs<14.5%) p = 0.242 p = 0.046 p = 0.881 Agatston CCS 3.457 [1.635-3.861],3.08 [0.927-10.235], 3.339 [1.854-6.015], (≥400 vs <400) p < 0.001 p =0.066 p < 0.001 EpAT: Epicardial Adipose Tissue; CCS: Agatston coronarycalcium score, CI: Confidence interval, FAI: Fat Attenuation Index, FPi:Fibrous Plaque index; HR: Hazard Ratio, HU: Hounsfield units, PVAT:Perivascular Adipose Tissue, PVWi: perivascular water index; VPCI:Volumetric Perivascular Characterisation Index

Survival analysis of the 16 deaths of unknown cause, identified coronarycalcification and EpAT volume as significant predictors of mortality (HR[95%]: 3.45 [1.28-9.28], p=0.014 for calcium-i, 4.24 [1.51-11.93],p=0.006 for Agatston score and 1.008 [1.001-1.015], p=0.018 for EpATvolume (in cm³). There was a non-significant trend for higher mortalitywith high FAI_(PVAT) values (HR [95% CI]: 1.98 [0.70-5.57], p=0.198). Nosignificant predictive value was found for VPCI (HR [95% CI]: 1.10[0.40-3.04], p=0.861), FPi (HR [95%]: 0.99 [0.91-1.07], p=0.74) orFAI_(EpAT) (HR [95% CI]: 1.02 [0.94-1.11], P=0.63) as predictors of thedeaths of unknown cause.

Multivariable adjustment for age, gender, traditional risk factors,presence of CAD, clinically relevant medication at baseline, CT scannertype and Agatston score (≥400 vs<400) identified FAI_(PVAT) as a strongindependent predictor of all-cause mortality, driven mainly by cardiacbut not non-cardiac mortality (Table 3, FIG. 5C). Indeed,FAI_(PVAT)≥−70.1 HU was linked to an almost two-fold increase in theadjusted risk for all-cause and to a more than five-fold increase in therisk for cardiac mortality over an average of 6.4 years, compared toindividuals in the low FAI_(PVAT) group. Notably, these effects wereindependent of the average radiodensity or total volume of the EpATdepot. EpAT volume, a marker of epicardial adiposity, was a significantpredictor of mortality, while fibrous plaque burden and vascularcalcification (measured as FPi and calcium-i in the proximal RCArespectively) were also identified as strong and independent predictorsof all-cause mortality.

TABLE 4 Multivariable Cox regression for prediction of all-cause,cardiac and non-cardiac mortality CTA-derived All-cause mortalityCardiac mortality Non-cardiac mortality indices HR[95% CI], P valueHR[95% CI], P value HR[95% CI], P value FAI_(PVAT) (≥70.1 1.786[1.058-3.014], 5.433 [1.642-17.976], 1.232 [0.631-2.407], HU vs <−70.1HU) p = 0.03 p = 0.006 p = 0.541 FAI_(EpAT) (per 1 1.048 [1.00-1.100],1.001 [0.904-1.107], 1.059 [0.997-1.125], HU increase) p = 0.052 p =0.991 p = 0.064 Calcium-i (>0 1.882 [1.179-3.005], 3.351 [1.241-9.044],1.593 [0.867-2.927], vs 0) p = 0.008 p = 0.017 p = 0.133 FPi (per 0.011.054 [1.021-1.088], 1.174 [1.092-1.263], 1.027 [0.986-1.070], unitincrease) p = 0.001 p < 0.001 p = 0.198 EpAT volume (per 1.008[1.004-1.012], 1.008 [0.998-1.018], 1.007 [1.001-1.013], 1 cm³ increase)p < 0.001 p = 0.137 p = 0.022 VPCI (≥14.5% 0.780 [0.512-1.188], 1.000[0.386-2.588], 0.739 [0.432-1.262], vs <14.5%) p = 0.247 p = 0.999 p =0.268 Agatston CCS 1.267 [0.717-2.240], 0.598 [0.172-2.077], 1.512[0.727-3.142], (≥400 vs <400) p = 0.416 p = 0.419 p = 0.268

Model adjusted for: age, gender, hypertension, hypercholesterolemia,diabetes mellitus, active smoker status, medications at baseline(antiplatelets, statins), presence of coronary artery disease, CTscanner used, Agatston CCS score (≥400 vs<400); CTA: Computed tomographyangiography; CCS: coronary calcium score, CI: Confidence interval, FAI:fat attenuation index, HR: hazard ratio, CI: Confidence interval; HU:Hounsfield units, PVAT: perivascular adipose tissue; EpAT: Epicardialadipose tissue; FPi: Fibrous plaque index; VPCI: Volumetric perivascularcharacterization index.

The OxScore

Next, all four imaging biomarkers that were found to be independentpredictors of mortality were combined to generate a novel cardiac CTArisk score that would be easy to calculate in routine clinical CTA, the“OxScore” (FIG. 3A). The four biomarkers that were included in the model(namely FAI_(PVAT), FPi, Calcium-I and EpAT-vol) describe differentaspects of cardiac and coronary physiology and can be calculated usingsemi-automated techniques on routine contrast CTA images. FAI_(PVAT) isa novel marker of vascular inflammation, while FPi and calcium-i reflectlocal structural disease by describing the presence of fibrous orcalcified/mixed plaques. Finally, EpAT-volume is a marker of epicardialadiposity, a well-established risk factor of adverse cardiometabolicevents.

Combination of these four indices into a combined model (OxScore)generated an individualised risk score for all-cause andcardiac-specific death (FIG. 6 ). Stratification of the study populationbased on the proposed model identified a high-risk subgroup(OxScore_(high)) with an almost eight-fold higher risk of all-causemortality compared to the low-risk group (OxScore_(low)). Similarly,application of the novel model identified a group of 192 studyparticipants with a significantly higher risk of cardiac death duringfollow-up compared to the low-risk subgroup of 1680 study participants(FIG. 6B-E).

Comparison of the OxScore Against Traditional Cardiac CT Indices

Next, the predictive value of the new OxScore model was compared againsttraditional risk factors and cardiac CT indices, including the presenceof high coronary calcium (as demonstrated by an Agatston score of ≥400versus <400) and obstructive CAD. Two predictive models were constructedas follows: Model 1: age, gender, hypertension, hypercholesterolemia,diabetes mellitus, current smoker status, CAD and Agatston score (≥400versus <400), Model 2: Model 1+OxScore variables (FAI_(PVAT), FPi,Calcium-I, EpAT volume). Both models were significant predictors ofall-cause and cardiac mortality, as demonstrated in ROC curve analysis(FIG. 7A, C). However, addition of the OxScore into the standard modelsignificantly improved the predictive power of the overall model(A[AUC]=0.031, P<0.05) with respect to all-cause mortality (FIG. 7A) andresulted in a net reclassification of 7.6% of the study population(NRI=7.6%), mainly by improving classification of non-events (FIG. 7B).By following a similar approach for cardiac mortality, inclusion ofOxScore resulted in an even more pronounced, significant improvement inthe predictive value of the model (Δ[AUC]=0.10, P<0.01) while alsoimproving cardiac risk classification (NRI=11.3%).

Validation of Perivascular Indices in Other Vessels

Finally, we explored whether perivascular indices such as perivascularwater index, can be measured along vessels other than the proximal RCA.FIG. 8 demonstrates how PVWi is calculated around different vessels.More specifically, PVWi is calculated along the right coronary artery(RCA) (FIG. 8A), left anterior descending artery (LAD) (FIG. 8B), leftcircumflex artery (LCx) (FIG. 8C), aorta (FIG. 8D) and the commoncarotid artery (FIG. 8E), respectively.

Discussion

In this study the present inventors demonstrate that a novel imagingbiomarker, that detects coronary artery inflammation by analysing thespatial changes of CT attenuation of peri-coronary adipose tissue(FAI_(PVAT)), is a powerful predictor of all-cause and cardiacmortality. As a previously validated biomarker of vascular inflammation,the new index advances significantly the current state of the art, byovercoming the limitations of calcium or fibrous plaque indices, thatare driven by non-reversible structural changes of the vascular wall. Bycombining FAI_(PVAT) with a number of structural biomarkers, derivedfrom the same segment of the coronary artery (Calcium-I, FPi) as well astotal EpAT volume, the inventors have created a new integrated CTA riskscore, the OxScore, that enables re-stratification of subjects in bothprimary and secondary prevention based on routine CTA, dissociating riskprediction from the simple presence of atherosclerotic plaques orcalcification. This new re-stratification can be applied bothprospectively and retrospectively in routine CTA imaging, and may guidethe targeted deployment of more aggressive preventive strategies to asignificant proportion of subjects where CTA does not reveal significantanatomical coronary artery disease, but the risk of future coronaryevents remains high.

Early, non-invasive diagnosis of vascular inflammation (an earlybiological process preceding plaque formation but also leading to plaquerupture) has been hailed as the “holy grail” of CAD diagnostics andcould help improve risk stratification in primary and secondaryprevention. However, the current state-of-the-art methods for thediagnosis of vascular inflammation and cardiovascular risk predictionare suboptimal and have several limitations. Circulating inflammatorybiomarkers (e.g. CRP, TNF-α) have a limited value in cardiovascular riskprediction since they are not specific to the cardiovascular system andhave poor correlation with local vascular inflammation (Weintraub etal., 2000; Lee et al., 2012; Margaritis et al., 2013). In the field ofcardiovascular imaging, the predictive value of Agatston coronarycalcium score measured by CT has been long-established (Greenland etal., 2004). However, coronary calcification represents a non-reversibleprocess that does not change in response to appropriate medical therapy(e.g. statins) (Alexopoulos et al., 2013). In fact, calcified plaquesare considered more stable and less likely to rupture compared toplaques with high-risk features, such as a thin-cap fibroatheromas and alarge necrotic core (Huang et al., 2001). Detection of high-risk plaquefeatures such as microcalcification, a large necrotic core or positiveremodelling on CTA have all been shown to predict future cardiac events(Hecht et al., 2015; Saremi et al., 2015) but the reliability of themethod is affected by the observer's expertise and CT settings andparameters, including spatial resolution (Maurovich-Horvat et al., 2014;Maurovich-Horvat et al., 2014; Fleg et al., 2012). Newer invasivemethods such as optical coherence tomography (OCT) and intravascularultrasound (IVUS) have been more successful in detecting high-riskplaques but are invasive, expensive, carry a small yet significant riskof in-procedure complications (Bezerra et al., 2009; McDaniel et al.,2011) and are not suitable for primary prevention and wide screening oflow-risk individuals. Positron emission tomography (PET) with ¹⁸F-FDG isexpensive, associated with significantly higher levels of radiationexposure compared to CT alone, not readily available and limited bymyocardial uptake of the radiotracer that results in significantbackground noise (Rogers et al., 2011; Joshi et al., 2014). Even theintroduction of newer radiotracers (such as ¹⁸F—NaF), althoughpromising, carries many of the limitations of traditional PET imaging,including but not limited to significant radiation exposure, limitedavailability and no demonstrated value in primary or even secondaryprevention (Joshi et al., 2014). Therefore, there is still need for afunctional biomarker that will describe vascular inflammation ratherthan structural and non-reversible changes in the vascular wall. Thisbiomarker should be easy to obtain through routine tests that arealready performed under the current clinical guidelines.

In their previous work, the present inventors have demonstrated thatFAI_(PVAT) is positively associated with the presence of CAD and thevolume of fibrous plaque in the proximal RCA independently of thepresence of coronary calcium. In the same study, it was shown thatFAI_(PVAT) changes in a dynamic way in response to local rupture of aculprit lesion in patients with acute MI and can distinguish culpritfrom non-culprit lesions. These observations supported the inventors'hypothesis that FAI_(PVAT) could function as a dynamic biomarker ofvascular inflammation and cardiovascular risk and offer diagnostic andprognostic information beyond that of traditional biomarkers, such ascoronary calcium.

In the current study the present inventors explore the predictive valueof FAI_(PVAT) along with other vascular/perivascular imaging biomarkersin a large prospective cohort of mid-low risk individuals undergoingcoronary CTA and a mean follow-up of 6.4 years. High FAI_(PVAT) wasfound to be a significant and independent predictor of all-cause andcardiac but not non-cardiac mortality, independently of age, gender,traditional cardiovascular risk factors, presence of CAD and coronarycalcium. The predictive value of FAI_(PVAT) appears to be driven bycardiac rather than non-cardiac mortality. This is in accordance withthe underlying biology, given that FAI_(PVAT) is believed to be affectedby local rather than systemic inflammation.

More importantly, this study is the first to describe the predictivevalue of peri-coronary adipose tissue quality characterized bynon-invasive CTA. Previous studies have described that lower attenuationof the visceral and subcutaneous adipose tissue depots on CT isassociated with adverse cardiometabolic effects independently of fatvolume (Rosenquist et al., 2013) while decreasing attenuation in thesame depots has more recently been associated with a deterioration oftraditional cardiovascular risk factors (Lee et al., 2016). Similarly,lower attenuation in the EpAT has been associated with high-risk plaquefeatures (Lu et al., 2016). In this regard, the findings of the presentinventors are radical, since they demonstrate an opposite, “paradoxical”trend for PVAT attenuation. However, these observations are in line withprevious studies of the present inventors on the interplay between thevascular wall and PVAT and the effects of vascular inflammation on PVATquality. Taken together, these findings suggest that local rather thansystemic factors affect PVAT quality, and contrary to other fat depots,PVAT quality can function as a “sensor” of inflammation in theunderlying coronary artery and therefore a specific predictor of adversecardiac events.

It is evident that FAI_(PVAT) describes a different vascular biologythan FPi and Calcium-i. While the latter two biomarkers reflectstructural changes of the vascular wall (namely fibrous plaque andvascular calcification respectively), FAI_(PVAT) is a dynamic marker ofvascular inflammation. Indeed, using multivariable cox regressionmodels, we demonstrated that FAI_(PVAT) is a strong predictor ofall-cause and cardiac mortality independently of FPi and calcium-i, evenafter adjustment for potential confounders, such as age, gender,epicardial fat volume, cardiovascular risk factors and clinicallyrelevant medication. On the contrary, the predictive value of calcium-ior Agatston score (current CTA biomarker recommended for riskstratification) is significantly reduced or eliminated in multivariablemodels after adjustment for age, suggesting that vascular calcification,is at least partly, a surrogate of ageing.

As previously discussed, current scoring systems for cardiovascular riskprediction often fail to detect “vulnerable subjects” for cardiac eventswithin populations of mid-low risk asymptomatic individuals. More thanhalf of ruptured plaques derive from lesions that were previouslyasymptomatic and non-obstructive (<50% stenosis) (Fishbein et al.,1996). Similar lesions are frequently seen on CT angiograms but there iscurrently no available method to identify which patients are athigh-risk and therefore in need of more aggressive medical intervention.A quick, reliable, easy-to-use and readily available method that woulddetect this group of patients would be invaluable in the clinicalsetting. In the current study, the present authors combined ourobservations on the predictive value of FAI_(PVAT) along with otherindices of the perivascular and vascular tissue into a novel score, theOxScore. The proposed scoring method takes into account traditionalstructural biomarkers of vascular disease (Calcium-i, FPi), adiposity(EpAT volume) and combines them with a novel functional index ofcoronary and perivascular tissue inflammation (FAI_(PVAT)) to generate apowerful tool for cardiovascular risk stratification. Overall, OxScorewas not only an independent predictor of future mortality, but moreimportantly improved risk stratification beyond the traditionalinterpretation of a CTA scan, that includes Agatston score and/or thepresence of obstructive CAD.

CONCLUSIONS

The present inventors have demonstrated a new imaging biomarker fordetection of coronary artery inflammation, through quantification of CTattenuation of peri-coronary adipose tissue. The new biomarker,Perivascular Fat Attenuation Index (FAI_(PVAT)), predicts all-cause andcardiac mortality independently of traditional risk factors, thepresence of CAD and coronary calcification. The present inventors nowpropose a novel CT-based risk score, the OxScore, that significantlyimproves cardiac risk stratification of low to mid-risk individualsundergoing routine CTA. Based on the current findings and itssimplicity, the method can even be applied retrospectively in existingscans and re-stratify populations who have been discharged following CTangiograms with non-obstructive disease. This method has the potentialto change clinical practice, establishing coronary CTA as a powerfulprognostic tool in both primary and secondary prevention.

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1.-26. (canceled)
 27. A method of predicting a mortality risk or risk of a patient suffering a cardiovascular event, said method comprising: (a) determining: (i) epicardial adipose tissue volume (EpAT-vol); and at least one of (ii) fat attenuation index of epicardial adipose tissue (FAI_(EpAT)); and (iii) fat attenuation index of perivascular adipose tissue (FAI_(PVAT)); wherein FAI_(PVAT) is determined using data gathered from a computer tomography (CT) scan along a length of a blood vessel; and (b) comparing each of the values determined in (a) to a pre-determined cut-off value or using the absolute value of each variable determined in (a) in order to generate an output value that indicates the patient's mortality risk or risk of suffering a cardiovascular event.
 28. The method according to claim 27, wherein step (a) further comprises using the data gathered from a computer tomography scan along a length of a blood vessel to determine (iv) calcium index (Calcium-i) and/or (v) fibrous plaque index (FPi); wherein the value of (iv) and/or (v) is included in step (b) of claim
 27. 29. The method according to claim 27, wherein step (a) further comprises using the data gathered from a computer tomography scan along a length of a blood vessel to determine (vi) perivascular water index (PVWi) and/or (vii) volumetric perivascular characterisation index (VPCI); wherein the value of (vi) and/or (vii) is included in step (b) of claim
 27. 30. The method according to claim 27, further comprising determining one or more of (viii) age and (ix) gender of the patient and wherein the value of (viii) and/or (ix) is included in step (b) of claim
 27. 31. The method according to claim 27, further comprising determining one or more of: (x) coronary calcium; (xi) hypertension; (xii) hyperlipidemia/hypercholesterolemia; (xiii) diabetes mellitus; (xiv) presence of coronary artery disease; (xv) smoking; and (xvi) family history of heart disease; and wherein the value of one or more of (x)-(xvi) is included in step (b) of claim
 27. 32. The method according to claim 27, further comprising determining one or more of: (iv) calcium index (Calcium-i); (v) fibrous plaque index (FPi); (vi) perivascular water index (PVWi); (vii) volumetric perivascular characterisation index (VPCI); (viii) age of the patient; (ix) gender of the patient; (x) coronary calcium; (xi) hypertension; (xii) hyperlipidemia/hypercholesterolemia; (xiii) diabetes mellitus; (xiv) presence of coronary artery disease; (xv) smoking; and (xvi) family history of heart disease; and wherein the value of one or more of (iv)-(xvi) is included in step (b) of claim
 27. 33. The method according to claim 27, wherein coefficients for each of (i) to (iii) are derived from Cox hazard or logistic regression models.
 34. The method according to claim 27, wherein the cut-off points for each of (i) to (v) are derived from receiver operating characteristic (ROC) curves.
 35. The method according to claim 27, wherein the output value is a continuous single valued function or a value that falls within one of three discrete brackets corresponding to low, medium and high risk of a cardiac event, cardiac death or all-cause mortality.
 36. The method according to claim 27, wherein the method is used to stratify patients according to their risk of all-cause or cardiac mortality.
 37. The method according to claim 27, wherein the patient has been diagnosed with vascular inflammation, or a condition known to be associated with vascular inflammation. 